This paper examines two specific aspects of stage 1 of the Bank for International Settlement’s (BIS’s) proposed reforms to the 8% risk-based capital ratio. We argue that relying on “traditional” agency ratings could produce cyclically lagging rather leading capital requirements, resulting in an enhanced rather than reduced degree of instability in the banking and financial system. Despite this possible shortcoming, we believe that sensible risk based weighting of capital requirements is a step in the right direction. The current risk based bucketing proposal, which is tied to external agency ratings, or possibly to internal bank ratings, however, lacks a sufficient degree of granularity. In particular, lumping A and BBB (investment grade corporate borrowers) together with BB and B (below investment grade borrowers) severely misprices risk within that bucket and calls, at a minimum, for that bucket to be split into two. We examine the default loss experience on corporate bonds for the period 1981–1999 and propose a revised weighting system which more closely resembles the actual loss experience on credit assets.

This paper traces developments in the credit risk measurement literature over the last 20 years. The paper is essentially divided into two parts. In the first part the evolution of the literature on the credit-risk measurement of individual loans and portfolios of loans is traced by way of reference to articles appearing in relevant issues of the Journal of Banking and Finance and other publications. In the second part, a new approach built around a mortality risk framework to measuring the risk and returns on loans and bonds is presented. This model is shown to offer some promise in analyzing the risk-return structures of portfolios of credit-risk exposed debt instruments.

We propose a relatively simple, accurate and flexible approach to forecasting the distribution of defaulted debt recovery outcomes. Our approach is based on mixtures of Gaussian distributions, explicitly conditioned on borrower characteristics, debt instrument characteristics and credit conditions at the time of default. Using Moody’s Ultimate Recovery Database, we show that our mixture specification yields more accurate forecasts of ultimate recoveries on portfolios of defaulted loans and bonds on an out-of-sample basis than popular regression-based estimates. Further, the economically interpretable outputs of our model provide a richer characterization of how conditioning variables affect recovery outcomes than competing approaches. The latter benefit is of particular importance in understanding shifts in the relative likelihood of extreme recovery outcomes that tend to be realized more frequently than observations near the distributional mean.

Surveys on the use of agency credit ratings reveal that some investors believe that rating agencies are relatively slow in adjusting their ratings. A well-accepted explanation for this perception on the timeliness of ratings is the through-the-cycle methodology that agencies use. According to Moody’s, through-the-cycle ratings are stable because they are intended to measure default risk over long investment horizons, and because they are changed only when agencies are confident that observed changes in a company’s risk profile are likely to be permanent. To verify this explanation, we quantify the impact of the long-term default horizon and the prudent migration policy on rating stability from the perspective of an investor – with no desire for rating stability. This is done by benchmarking agency ratings with a financial ratio-based (credit-scoring) agency-rating prediction model and (credit-scoring) default-prediction models of various time horizons. We also examine rating-migration practices. The final result is a better quantitative understanding of the through-the-cycle methodology. By varying the time horizon in the estimation of default-prediction models, we search for a best match with the agency-rating prediction model. Consistent with the agencies’ stated objectives, we conclude that agency ratings are focused on the long term. In contrast to one-year default prediction models, agency ratings place less weight on short-term indicators of credit quality. We also demonstrate that the focus of agencies on long investment horizons explains only part of the relative stability of agency ratings. The other aspect of through-the-cycle methodology – agency-rating migration policy – is an even more important factor underlying the stability of agency ratings. We find that rating migrations are triggered when the difference between the actual agency rating and the model predicted rating exceeds a certain threshold level. When rating migrations are triggered, agencies adjust their ratings only partially, consistent with the known serial dependency of agency-rating migrations.

The most fundamental aspect of credit risk models is the rating of the underlying assets and the associated expected and unexpected migration patterns. The most important migration is to default. While default rate empirical studies of corporate bonds are now commonplace, there has never been a study on the default rate in the corporate bank loan market. This paper assesses, for the first time, the default rate experience on large, syndicated bank loans. The results are stratified by original loan rating using a mortality rate framework for the 1991–1996 period. We find that the mortality rates on bank loans are remarkably similar to that of corporate bonds when measured cumulatively over the five-year period after issuance, but loan default rates appear to be considerably higher than bonds for the first two years after issuance. Since loans have an average effective maturity of under two years, this shorter maturity difference is of considerable relevance, especially if confirmed when our database will cover a longer sample period and more actual defaults. We do attempt to estimate the impact and bias on our results of the study's sample period which only covers the recent benign credit cycle in the US. Our results provide important new information for assessing the risk of corporate loans not only for bankers but also mutual fund investors and analysts of structured financial products, credit derivatives and credit insurance.